A theoretical & empirical analysis of evolutionary testing and hill climbing for structural test data generation

Evolutionary testing has been widely studied as a technique for automating the process of test case generation. However, to date, there has been no theoretical examination of when and why it works. Furthermore, the empirical evidence for the effectiveness of evolutionary testing consists largely of small scale laboratory studies. This paper presents a first theoretical analysis of the scenarios in which evolutionary algorithms are suitable for structural test case generation. The theory is backed up by an empirical study that considers real world programs, the search spaces of which are several orders of magnitude larger than those previously considered.